(provided by candidate): Borderline Personality Disorder (BPD) is a complex psychiatric disorder associated with subjective misery, poor quality of life, and chronic suicidality, with estimates of suicide completion as high as 10%. Because of aggressive, suicidal, and self-injurious behaviors, treatment utilization among BPD patients is significantly higher than patients with mood disorders or other personality disorders, resulting in significant treatment costs. Although this disorder affects 1-2% of the population, individuals with BPD account for 10-20% of all outpatients and 15% of inpatients. Despite the significant societal burden, subjective distress, and poor psychiatric outcomes associated with BPD, the phenotype is not well understood relative to other forms of psychopathology. Improving the construct validity of BPD has been impeded, in part, by the considerable heterogeneity reflected in theoretical conceptualizations and the DSM diagnostic criteria. Existing diagnostic criteria define a meaningful, but diffuse, construct and future research efforts will be impeded if patients are selected using these criteria alone. The proposed research seeks to refine the BPD phenotype by exploring additional clinical and experimental markers, as well as using flexible latent variable modeling and statistical simulation to clarify the classification of the disorder. This work will result in an empirically derived taxonomy of BPD consisting of traits, subtypes, or both, as well as data-based thresholds for identifying clinically significant borderline pathology. This research has the potential to identify more homogeneous groups of BPD patients, which will increase the resolving power and replicability of future empirical work. Additionally, clarifying the heterogeneity among BPD patients will improve future treatment research, as clinical strategies can be tailored better to individual patients.
The specific aims are: 1) to assess the efficiency of extant analytic methods for resolving the latent structure of BPD using Monte Carlo simulation methods, 2) to clarify within-BPD heterogeneity using hybrid latent variable models that integrate continuous and categorical conceptions, and 3) to refine the BPD phenotype by exploring additional clinical and experimental markers. This proposed research will be conducted using two major epidemiological datasets (the National Comorbidity Survey Replication and the National Epidemiologic Survey on Alcohol and Related Conditions) and a large sample of 616 participants aggregated from five prior studies, approximately 25% of whom have BPD. In addition, an experimental psychopathology measure of rejection sensitivity will be developed and validated using a smaller sample of 168 adult participants, and this measure will be integrated into classification models. We expect that the DSM-IV BPD features are best described by a single continuous dimension of severity, but that subtypes of the disorder will be identified on the basis of additional markers, including aggression, mistrustfulness, preoccupied attachment, and rejection sensitivity. Competing dimensional, categorical, and integrative models will be tested.

Public Health Relevance

Borderline Personality Disorder (BPD) is a complex psychiatric disorder associated with aggressive, suicidal, and self-injurious behaviors that often require intensive long-term treatment, resulting in significant treatment costs. Although this disorder is disproportionately represented in inpatient and outpatient settings relative to its prevalence in the population, patients diagnosed with this condition are so heterogeneous that it has been difficult to identify its causes or to design effective treatments. The proposed research seeks to refine the prevailing conception of BPD using sophisticated statistical models and epidemiological samples, which will permit better classification of BPD, resulting in improved treatment outcomes for patients.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1-F12B-S (20))
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Rubio, Mercedes
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University of Pittsburgh
Schools of Medicine
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Wright, Aidan G C; Hallquist, Michael N (2014) Mixture modeling methods for the assessment of normal and abnormal personality, part II: longitudinal models. J Pers Assess 96:269-82
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